We humans are fundamentally storytellers. We like to organize events into chains of causes and effects that explain the consequences of our actions. We like to assign credit and blame. This makes sense from an evolutionary standpoint. The ultimate job of our nervous system is to make actionable decisions, and predicting the consequences of those decisions is important to our survival.

Science is a rich source of powerful explanatory stories. For example, Newton explained how a force causes a mass to accelerate. This gives us a story of how an apple drops from a tree or a planet circles around the Sun. It allows us to decide how hard the rocket engine needs to push to get it to the Moon. Models of causation allow us to design complex machines like factories and computers that have fabulously long chains of causes and effects. They convert inputs into the outputs that we want.

It is tempting to believe that our stories of causes and effects are how the world works. Actually, they are just a framework that we use to manipulate the world and to construct explanations for the convenience of our own understanding. For example, Newton's equation, F= Ma, does not really say that force causes acceleration any more than it says that mass causes force. We humans tend to think of force as contingent, because we often have the choice as to whether to apply it or not. On the other hand, we tend to think of mass as not being under our control. Thus, we personify nature, imagining it almost as if natural forces are deciding to push on masses. It is much harder for us to imagine accelerations deciding to cause mass, so we tell the story a certain way. We credit gravitational force for keeping the planets orbiting around the Sun, and blame it for pulling the apple down from the tree.

This convenient personification of nature helps us use our mental storytelling machinery to explain the natural world. The cause-and-effect paradigm works particularly well when science is used for engineering, to arrange the world for our convenience. In this case, we can often set things up so that the illusion of cause-and-effect is almost a reality. The computer is a perfect example. The key to what makes a computer work is that the inputs affect the outputs, but not vice versa. The components used to construct the computer are constructed to create that same one-way relationship. These components, such as logic gates, are specifically designed to convert contingent inputs into predictable outputs. In other words, the logic gates of the computer are constructed to be atomic building blocks of cause-and-effect.

The notion of cause-and-effect breaks down when the parts that we would like to think of as outputs affect the parts that we would prefer to think of as inputs. The paradoxes of quantum mechanics are a perfect example of this, where our mere observation of a particle can "cause" a distant particle to be in a different state. Of course there is no real paradox here, there is just a problem with trying to apply our storytelling framework to a situation where it does not match.

Unfortunately, the cause-and-effect paradigm does not just fail at the quantum scale. It also falls apart when we try to use causation to explain complex dynamical systems like the biochemical pathways of a living organism, the transactions of an economy, or the operation of the human mind. These systems all have patterns of information flow that defy our tools of storytelling. A gene does not "cause" the trait like height, or a disease like cancer. The stock market did not go up "because" the bond market went down. These are just our feeble attempts to force a storytelling framework onto systems that do not work like stories. For such complex systems, science will need more powerful explanatory tools, and we will learn to accept the limits of our old methods of storytelling. We will come to appreciate that causes and effects do not exist in nature, that they are just convenient creations of our own minds.